What Are Data Labeling Tools?
Data labeling tools help convert raw, unstructured data, such as images, video, or text, into structured formats that AI algorithms can interpret. These tools tag or annotate data, making it usable for training models.
A Glimpse of a Data Labeling Tool. (Source)
Many of these tools use a human-in-the-loop approach, combining automation with human reviews to ensure accuracy. This teamwork helps create high-quality training datasets - the foundation for AI innovations like computer vision models, large language models, and generative AI models.
Data labeling tools are primarily classified into two key categories. Here’s a closer look at each of them:
- Open-Source Frameworks: Open-source tools are free to use and provide full access to the platform’s code, so you can customize them to fit your needs. They can be modified and shared under open licenses, but setup and maintenance usually require substantial technical expertise.
- Proprietary Platforms: These are commercial, fully managed solutions with built-in automation and analytics. Vendor support makes them easier to deploy and scale, but they offer limited customization flexibility on the user side.
Open-Source Vs. Proprietary Data Labeling Platforms
When you compare open-source and proprietary data labeling platforms, it’s clear that both have their own unique advantages. Open-source tools give you more flexibility and control, but they usually need more technical skills to set up and maintain. Proprietary platforms are easier to use and come with built-in features like automation and support, but they’re less customizable.
As shown in the table below, the best choice depends on what your team needs. That’s why many organizations end up looking for a mix of both.
Differences Between Open-Source and Proprietary Data Labeling Tools
When Open-Source Data Labeling Tools Are the Right Fit
Now that we have a better understanding of what an open-source data labeling tool is, let’s explore some real-world examples where using open-source tools can provide the most value.
Using Open-Source Data Labeling Tools in Academic Settings
Not all AI projects require massive datasets or large teams. In academic or personal projects, especially those using transfer learning and pre-trained models, the amount of annotated data needed can be considerably smaller.
For example, a student working on an object detection task might only need to annotate around 300 images. In these simpler use cases, it makes more sense to use lightweight, open-source tools that are easy to set up and free to use.
Tools like LabelStudio and MakeSense are ideal for this. They support essential annotation types like bounding boxes and polygons, offer user-friendly interfaces, and don’t require heavy technical infrastructure - making them perfect for small-scale projects with limited resources.
A Look at LabelStudio (Source)
Data Labeling Platforms for Early-Stage AI Startups
Early-stage startups often operate with small teams and limited budgets. Their main goal is speed - getting prototypes up and running without added complexity. At this point, datasets are typically small or highly specific, like a few hundred user queries. The perfect data labeling tool should be easy to set up, help teams iterate quickly, and keep the focus on validating ideas.
For instance, a startup developing an AI-powered customer support chatbot might need to annotate text data to detect user intent, extract key information, or flag urgent messages. Open-source tools like Doccano and LightTag handle these tasks well.
An example of using Doccano for text annotations. (Source)
When Proprietary Data Labeling Platforms Are a Better Fit
Academic projects and prototypes usually deal with smaller datasets and focus on learning or experimentation. But real-world, production-grade AI projects need to handle larger volumes of data and deliver reliable, efficient results.
Next, let’s look at a couple of examples where proprietary data labeling platforms might be the better choice.
Data Labeling Tools for AI in Healthcare Projects
Healthcare projects that use AI often require high precision. Let’s say a company is developing a model to detect skin lesions. They might be working with thousands of high-resolution images, where even the smallest details, like asymmetry, slight color changes, or irregular borders, can be crucial.
An example of data labeled for dermatology-related applications. (Source)
In this case, they need an annotation tool that can handle both the scale and sensitivity of the project. On top of that, regulations like HIPAA add strict requirements for data privacy and security.
This is where a platform like TensorAct Studio is especially useful. It supports detailed image annotation for tasks like classification and segmentation, along with secure data handling and customizable workflows.
With AI-assisted labeling, version control, and built-in quality checks, teams can manage complex tasks efficiently - without compromising on accuracy or data compliance. Whether in healthcare or any other industry where precision and security matter, TensorAct Studio provides the control and flexibility needed to deliver reliable, high-quality results.
Proprietary Data Labeling Platforms for E-commerce Workflows
Another area where a proprietary tool like TensorAct Studio proves reliable is e-commerce. Online retail platforms manage millions of product images that must be accurately labeled and continuously updated. This goes far beyond basic tagging - it demands fast, high-precision annotation at scale.
During peak times like seasonal launches or sales events, companies may need to classify tens of thousands of images by attributes such as color, category, material, and brand - often within just a few days. Errors or delays in labeling can disrupt product search, weaken recommendations, and impact sales.
TensorAct Studio is built to handle this level of demand. When accurate product data directly affects customer experience, a dependable proprietary platform delivers the speed, precision, and integration needed to keep everything running smoothly.
Autonomous Vehicle Projects Driven By 3D Data Labeling Tools
So far, we've discussed scenarios that involve image or text annotation, but some AI projects rely on more complex data types, like LiDAR. LiDAR (Light Detection and Ranging) uses laser pulses to create detailed 3D maps of the environment. It’s a key technology in autonomous vehicles, helping them detect objects, measure distances, and understand their surroundings with high precision.
Annotating LiDAR data, especially in the form of 3D point clouds, is far more complex than labeling images or text. Generally, developers working on Level 4 autonomous vehicles must label millions of data points to identify features like lane markings, curbs, moving vehicles, and pedestrians, all in 3D space and often aligned with camera footage.
Annotating LiDAR data to train models for deployment in self-driving cars. (Source)
This level of detail is difficult to manage with open-source tools. Proprietary platforms like TensorAct Studio offer the advanced capabilities needed for these tasks - such as 3D annotation tools, sensor fusion, machine learning integration, and version-controlled workflows. For safety-critical applications like autonomous driving, proprietary tools deliver the accuracy, scalability, and control required to build reliable systems.
Blending Open-Source and Proprietary Tools for Smarter Labeling
If your AI project doesn’t fit within the scenarios we’ve walked through so far, a hybrid data labeling approach might be the right fit. This means using both open-source and proprietary tools - switching between them as your project evolves, like a hybrid car switching between electric and gas.
Open-source tools might work well in the early stages. They’re flexible, cost-effective, and great for experimenting or handling large datasets when things are still taking shape.
But as your project moves toward production, the project’s needs can change. You may need faster, more accurate labeling that meets strict quality and industry standards. That’s where proprietary tools can be brought in.
Factors to Consider When Choosing a Data Labeling Tool
Here are some other key factors to consider when choosing a data labeling tool:
- Project size and complexity: Big or detailed projects often need robust, scalable tools (usually proprietary). Smaller or more flexible projects might do better with open-source tools that you can customize.
- Budget flexibility: Open-source tools are free, but they may take more time and effort to set up. Proprietary tools cost money, but they usually come with built-in features, support, and faster setup.
- Data security: If you're working with sensitive or private data, security is critical. Open-source tools can be hosted on your own servers, giving you full control over where your data goes. Proprietary tools usually offer secure cloud storage and follow strict privacy rules (like GDPR or HIPAA), which can help with compliance.
- Tool integration: Open-source tools allow deep customization but take time. Proprietary options offer plug-and-play integrations for faster setup.
- Team skills and size: If you have a strong technical team, open-source tools can work well. If your team is small or less technical, proprietary tools are easier to use and manage.
Key Considerations to Make When Selecting a Data Labeling Solution.
The Path to Reliable AI Starts with Data Labeling
Choosing between open-source, proprietary, or hybrid data labeling solutions isn’t a one-size-fits-all decision. It’s a lot like picking the right climbing gear - the gear that works for one terrain might not suit another. Some projects demand complete control and flexibility. Others need speed, built-in support, and structured workflows.
Ultimately, the right solution shapes your AI pipeline's quality, consistency, and long-term reliability. It's not just about data labeling; it’s about building trust in your AI solution.
At Objectways, we help teams make these decisions with clarity. Whether starting from scratch or refining a complex deployment, we support your goals with accurate data labeling services and adaptable AI solutions.
Have a vision for your perfect AI solution? Contact us today!
Frequently Asked Questions
- What is a data labeling platform?
- A data labeling platform is software that lets users annotate, manage, and organize raw data like images, text, or video for machine learning. It often includes quality control, collaboration tools, and automation features.
- What does a data labeling service offer?
- A data labeling service combines expert annotators with advanced tools to tag large datasets accurately. These services support AI development across industries like healthcare, automotive, and retail.
- Why are machine learning data labeling services essential?
- They ensure accurate, consistent annotations so AI models can detect patterns and make reliable predictions. Poor labeling leads to errors and weak model performance.
- What are the best tools for data labeling?
- It depends on your project needs, but TensorAct Studio is a good all-around option. It supports multiple data types, offers no-code/low-code workflows, and scales well from small projects to production.